A two-layer predictive control for hybrid electric vehicles energy management

Stroe, Nicoleta; Olaru, Sorin; Colin, Guillaume; Bencherif, Karim; Chamaillard, Yann

doi:10.1016/j.ifacol.2017.08.1777

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…The preview horizon N λ for the tuning block can vary between 5s and 60s and its choice depends mostly on the availability of navigation data and on drive cycle characteristics. A sensitivity analysis with respect to tuning horizon N λ , as well as a robustness study can be found in [36]. Here, the value that provides the best results was retained for each scenario.…”

Section: Simulations and Results For Mpc With Sands Functionalitymentioning

confidence: 99%

Predictive Control Framework for HEV: Energy Management and Free-Wheeling Analysis

Stroe¹,

Olaru²,

Colin³

et al. 2019

IEEE Trans. Intell. Veh.

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The focus of this work is to exploit the potential of model predictive control for the energy management of hybrid electric vehicles. The global framework covers the power distribution between the internal combustion engine and the electric machine(s) in order to minimize the fuel consumption. Current technologies allow the forecast of speed profile and road grade making predictive control a viable solution. The gears within the prediction model are assumed to be pre-calculated at a higher level and therefore, only the torque split problem needs to be addressed by the energy management mechanism. Engine stop can improve fuel gain which is even more noticeable for hybrid vehicles and hence, its inclusion is naturally considered in the present study. Furthermore, optimization of the speed profile is addressed by introducing the freewheeling functionality.

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Section: Simulations and Results For Mpc With Sands Functionalitymentioning

confidence: 99%

Predictive Control Framework for HEV: Energy Management and Free-Wheeling Analysis

Stroe¹,

Olaru²,

Colin³

et al. 2019

IEEE Trans. Intell. Veh.

Self Cite

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“…As stated by T. El Hajji et al [18], the sensitivity analysis can be conducted using different approaches based on a quantitative method, as published by Y. Zheng [19], or a qualitative method, as shown by N. Stroe et al [20].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Targeting Energy Efficiency through Air Conditioning Operational Modes for Residential Buildings in Tropical Climates, Assisted by Solar Energy and Thermal Energy Storage. Case Study Brazil

et al. 2021

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Economy and parsimony in the consumption of energy resources are becoming a part of common sense in practically all countries, although the effective implementation of energy efficiency policies still has a long way to go. The energy demand for residential buildings is one of the most significant energy sinks. We focus our analysis on one of the most energy-consuming systems of residential buildings located in regions of tropical climate, which are cooling systems. We evaluate to which degree the integration of thermal energy storage (TES) and photovoltaic (PV) systems helps to approach an annual net zero energy building (NZEB) configuration, aiming to find a feasible solution in the direction of energy efficiency in buildings. To conduct the simulations, an Energy Efficiency Analysis Framework (EEAF) is proposed. A literature review unveiled a potential knowledge gap about the optimization of the ASHRAE operational modes (full storage load, load leveled, and demand limiting) for air conditioning/TES sets using PV connected to the grid. A hypothetical building was configured with detailed loads and occupation profiles to simulate different configurations of air conditioning associated with TES and a PV array. Using TRNSYS software, a set of scenarios was simulated, and their outputs are analyzed in a life cycle perspective using life cycle costing (LCC). The modeling and simulation of different scenarios allowed for identifying the most economic configurations from a life cycle perspective, within a safe range of operability considering the energy efficiency and consequently the sustainability aspects of the buildings. The EEAF also supports other profiles, such as those in which the occupancy of residential buildings during the day is increased due to significant changes in people’s habits, when working and studying in home office mode, for example. These changes in habits should bring a growing interest in the adoption of solar energy for real-time use in residential buildings. The results can be used as premises for the initial design or planning retrofits of buildings, aiming at the annual net zero energy balance.

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“…To eliminate the loop on finding λ * t , it is considered that the optimal energy consumption corresponds in general to driving slow, so it can be assumed that the vehicle will use the entire travel time, i.e. (29) and for a given λ t , it holds, (31) i.e. the optimal travel time at the end of the horizon does not change for different SHMPC updates.…”

Section: Updating the Time Costate Over The Mpc Loopmentioning

confidence: 99%

Computationally efficient algorithm for eco-driving over long look-ahead horizons

Hamednia¹,

Sharma²,

Murgovski³

et al. 2020

Preprint

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This paper presents a computationally efficient algorithm for eco-driving over long prediction horizons. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables.To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin's Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to standard cruise control, the energy savings of using the proposed algorithm is up to 15.71 %.

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A two-layer predictive control for hybrid electric vehicles energy management

Cited by 10 publications

References 12 publications

Predictive Control Framework for HEV: Energy Management and Free-Wheeling Analysis

Predictive Control Framework for HEV: Energy Management and Free-Wheeling Analysis

Targeting Energy Efficiency through Air Conditioning Operational Modes for Residential Buildings in Tropical Climates, Assisted by Solar Energy and Thermal Energy Storage. Case Study Brazil

Computationally efficient algorithm for eco-driving over long look-ahead horizons

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